769 lines
32 KiB
Plaintext
769 lines
32 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pypsa\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"plt.style.use(\"ggplot\")\n",
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"import pycountry\n",
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"import json\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"available_models = {\n",
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" \"model_1\": \"elec_s_37_ec_lv1.0_.nc\", \n",
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" \"model_2\": \"elec_s_37_ec_lv1.0_3H_withUC.nc\",\n",
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" \"model_3\": \"elec_s_37_ec_lv1.0_Co2L-noUC-noCo2price.nc\",\n",
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" \"model_4\": \"elec_s_37_ec_lv1.0_Ep.nc\", \n",
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" \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\", \n",
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"}\n",
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"\n",
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"model_choice = \"model_5\"\n",
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"\n",
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"data_path = Path.cwd() / \"..\" / \"..\"\n",
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"model_path = data_path / available_models[model_choice]\n",
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"\n",
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"with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n",
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" pypsa_generation_mapper = json.load(f)\n",
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"\n",
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"plot_path = data_path / \"plots\" / available_models[model_choice][:-3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"os.mkdir(data_path / \"plots\" / available_models[model_choice][:-3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"n = pypsa.Network(str(model_path))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def intersection(alist, blist):\n",
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" total_list = list()\n",
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" for val in alist:\n",
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" if val in blist:\n",
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" total_list.append(val)\n",
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" return total_list "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pypsa_generation_mapper"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"color_mapper = pd.read_csv(\"color_mapper.csv\", index_col=0).iloc[:,0]\n",
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"color_mapper.loc[\"Others\"] = \"#D3D3D3\"\n",
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"color_mapper.loc[\"Storage Charge\"] = \"#51dbcc\"\n",
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"color_mapper.loc[\"Storage Discharge\"] = \"#51dbcc\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"color_mapper"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries = set([col[:2] for col in n.generators_t.p.columns])\n",
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"gen = set([col[6:] for col in n.generators_t.p.columns])\n",
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"\n",
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"for i, country in enumerate(countries):\n",
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"\n",
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" df = pd.DataFrame(index=n.generators_t.p.index)\n",
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" # country_generation = [col for col in n.generators_t.p.columns if col.startswith(country)]\n",
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" country_generation = n.generators.loc[n.generators.bus.str.contains(country)]\n",
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"\n",
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" for key, gens in pypsa_generation_mapper.items():\n",
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"\n",
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" # curr_gen = country_generation.loc[\n",
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" # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n",
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" curr_gen = country_generation.loc[\n",
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" country_generation.carrier.apply(lambda carr: carr in gens)\n",
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" ].index\n",
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"\n",
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" if len(curr_gen):\n",
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" df[key] = n.generators_t.p[curr_gen].mean(axis=1) \n",
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" else:\n",
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" df[key] = np.zeros(len(df))\n",
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"\n",
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" df.to_csv(data_path / \"pypsa_data\" / (country+\".csv\"))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_inflow_cols = ['Solar', 'Wind Onshore', 'Nuclear', 'Lignite', 'Inflow Lines', 'Inflow Links', 'Wind Offshore', 'Biomass', 'Run of River', 'Hydro', 'Hard Coal', 'Gas', 'Oil']\n",
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"total_outflow_cols = ['Outflow Links', 'Outflow Lines', 'Storage Charge']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_inflow_set = set()\n",
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"total_outflow_set = set()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"\n",
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"index = n.generators_t.p.index\n",
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"\n",
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"pypsa_total_inflow = pd.DataFrame(np.zeros((len(index), len(total_inflow_cols))),\n",
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" index=index, columns=total_inflow_cols)\n",
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"entsoe_df = pd.read_csv(data_path / \"harmonised_generation_data\" / (\"prepared_DE.csv\"),\n",
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" parse_dates=True,\n",
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" index_col=0)\n",
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"entsoe_total_inflow = pd.DataFrame(np.zeros((len(entsoe_df), len(total_inflow_cols))),\n",
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" index=entsoe_df.index, columns=total_inflow_cols)\n",
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"pypsa_total_outflow = pd.DataFrame(np.zeros((len(index), len(total_outflow_cols))),\n",
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" index=index, columns=total_outflow_cols)\n",
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"total_load = pd.Series(index=index)\n",
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"\n",
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"for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n",
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"\n",
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" # country = \"DE.csv\"\n",
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" cc = country[:2]\n",
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"\n",
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" country_buses = np.unique(n.generators.loc[n.generators.bus.str.contains(cc)].bus.values)\n",
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" print(f\"Buses for country {country[:-4]}: \", country_buses)\n",
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"\n",
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" if not len(country_buses) == 1:\n",
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" print(\"Current implementation is for one bus per country\")\n",
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" print(f\"Skipping!\")\n",
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" continue\n",
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"\n",
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" bus = country_buses[0]\n",
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"\n",
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" \"\"\" \n",
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" pypsa_df = pd.read_csv(data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0)\n",
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" \"\"\" \n",
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" try:\n",
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" entsoe_df = pd.read_csv(data_path / \"harmonised_generation_data\" / (\"prepared_\"+country),\n",
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" parse_dates=True,\n",
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" index_col=0)\n",
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"\n",
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" entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n",
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" entsoe_df = entsoe_df.iloc[1:]\n",
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" entsoe_df = entsoe_df.multiply(1e-3)\n",
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" except FileNotFoundError:\n",
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" continue \n",
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" \n",
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" fig, axs = plt.subplots(4, 3, figsize=(20, 20))\n",
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"\n",
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" axs[0,0].set_title(pycountry.countries.get(alpha_2=country[:2]).name)\n",
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"\n",
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" start = pd.Timestamp(\"2019-01-01\") # for small time frame\n",
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" end = pd.Timestamp(\"2019-01-14\")\n",
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" coarse_freq = \"3d\"\n",
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"\n",
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" num_techs_shown = 6\n",
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"\n",
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" energy_inflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
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" energy_outflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
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"\n",
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" # add generation\n",
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" country_gen = n.generators.loc[n.generators.bus == bus]\n",
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"\n",
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" for tech, pypsa_carrier in pypsa_generation_mapper.items():\n",
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" gens = country_gen.loc[country_gen.carrier.apply(lambda c: c in pypsa_carrier)].index\n",
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" energy_inflow[tech] = n.generators_t.p[gens].sum(axis=1)\n",
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"\n",
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" # add inflows from lines\n",
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" lines0 = n.lines.loc[n.lines.bus0 == bus].index\n",
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" lines1 = n.lines.loc[n.lines.bus1 == bus].index\n",
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"\n",
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" lines_flow = np.zeros(energy_inflow.shape[0])\n",
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" if not lines0.empty:\n",
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" lines_flow = - n.lines_t.p0[lines0].sum(axis=1)\n",
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"\n",
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" if not lines1.empty:\n",
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" lines_flow -= n.lines_t.p1[lines1].sum(axis=1)\n",
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"\n",
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" energy_inflow[\"Inflow Lines\"] = np.maximum(np.zeros_like(lines_flow), lines_flow)\n",
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" energy_outflow[\"Outflow Lines\"] = np.minimum(np.zeros_like(lines_flow), lines_flow)\n",
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"\n",
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" # add inflows from links\n",
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" links0 = n.links.loc[n.links.bus0 == bus].index\n",
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" links1 = n.links.loc[n.links.bus1 == bus].index\n",
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"\n",
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" links_flow = np.zeros(energy_inflow.shape[0])\n",
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" if not links0.empty:\n",
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" links_flow = - n.links_t.p0[links0].multiply(n.links.loc[links0, \"efficiency\"]).sum(axis=1)\n",
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"\n",
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" if not links1.empty:\n",
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" links_flow -= n.links_t.p1[links1].multiply(n.links.loc[links1, \"efficiency\"]).sum(axis=1)\n",
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"\n",
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" energy_inflow[\"Inflow Links\"] = np.maximum(np.zeros_like(links_flow), links_flow)\n",
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" energy_outflow[\"Outflow Links\"] = np.minimum(np.zeros_like(links_flow), links_flow)\n",
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"\n",
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" storage = n.storage_units.loc[n.storage_units.bus == bus].index\n",
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" if not storage.empty:\n",
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" storage_p = n.storage_units_t.p[storage].sum(axis=1).values\n",
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" # energy_inflow[\"Storage Discharge\"] = np.maximum(np.zeros_like(links_flow), storage_p)\n",
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" energy_inflow[\"Hydro\"] = energy_inflow[\"Hydro\"].values + np.maximum(np.zeros_like(links_flow), storage_p)\n",
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" energy_outflow[\"Storage Charge\"] = np.minimum(np.zeros_like(links_flow), storage_p)\n",
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"\n",
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" energy_inflow = energy_inflow.iloc[:-1].multiply(1e-3)\n",
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" energy_outflow = energy_outflow.iloc[:-1].multiply(1e-3)\n",
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" load = n.loads_t.p_set[bus].iloc[:-1].multiply(1e-3)\n",
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"\n",
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" total_load = total_load.loc[load.index]\n",
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" total_load = total_load + load\n",
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"\n",
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" pypsa_total_inflow = pypsa_total_inflow.loc[energy_inflow.index]\n",
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" pypsa_total_inflow[energy_inflow.columns] = (\n",
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" pypsa_total_inflow[energy_inflow.columns] + energy_inflow\n",
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" )\n",
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"\n",
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" pypsa_total_outflow = pypsa_total_outflow.loc[energy_outflow.index]\n",
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" pypsa_total_outflow[energy_outflow.columns] = (\n",
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" pypsa_total_outflow[energy_outflow.columns] + energy_outflow\n",
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" )\n",
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"\n",
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" entsoe_total_inflow = entsoe_total_inflow.loc[entsoe_df.index]\n",
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" entsoe_total_inflow[entsoe_df.columns] = (\n",
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" entsoe_total_inflow[entsoe_df.columns] + entsoe_df.fillna(0.)\n",
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" )\n",
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"\n",
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" show_techs = energy_inflow.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
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" others = energy_inflow.sum().sort_values(ascending=False).iloc[num_techs_shown:].index.tolist()\n",
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" # show_techs = entsoe_df.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
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"\n",
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" show_techs = intersection(show_techs, entsoe_df.columns.tolist())\n",
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" entsoe_df[\"Others\"] = entsoe_df.drop(columns=show_techs).sum(axis=1)\n",
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"\n",
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" # entsoe_df[show_techs + [\"Others\"]].loc[start:end].plot.area(ax=axs[0,0])\n",
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" index = load.loc[start:end].index\n",
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"\n",
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" entsoe_df.index = load.index\n",
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"\n",
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" energy_inflow[\"Others\"] = energy_inflow.drop(columns=show_techs).sum(axis=1)\n",
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"\n",
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" # plot timeframe\n",
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" axs[0,0].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n",
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" axs[0,1].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2)\n",
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"\n",
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" axs[0,1].stackplot(index,\n",
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" *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
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" colors=color_mapper.loc[show_techs + [\"Others\"]].tolist()\n",
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" )\n",
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" axs[0,1].stackplot(index, *[energy_outflow[col].loc[index].values for col in energy_outflow.columns],\n",
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" colors=color_mapper.loc[energy_outflow.columns].tolist(),\n",
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" labels=energy_outflow.columns\n",
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" )\n",
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"\n",
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" axs[0,0].stackplot(index, *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
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" labels=show_techs+[\"Others\"],\n",
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" colors=color_mapper.loc[show_techs + [\"Others\"]].tolist())\n",
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" axs[0,1].plot(index,\n",
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" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values + energy_outflow.loc[index].sum(axis=1).values,\n",
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" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n",
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"\n",
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" axs[0,0].legend()\n",
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" axs[0,1].legend()\n",
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"\n",
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" # plot whole year\n",
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"\n",
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" index = load.resample(coarse_freq).mean().index\n",
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"\n",
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" axs[1,0].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n",
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" axs[1,1].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2)\n",
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"\n",
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" axs[1,1].stackplot(index, *[energy_inflow[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]],\n",
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" colors=color_mapper.loc[show_techs + [\"Others\"]].tolist())\n",
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" axs[1,1].stackplot(index, *[energy_outflow[col].resample(coarse_freq).mean().values for col in energy_outflow.columns],\n",
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" colors=color_mapper.loc[energy_outflow.columns].tolist(),\n",
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" labels=energy_outflow.columns\n",
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" )\n",
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"\n",
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" axs[1,0].stackplot(index, *[entsoe_df[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]],\n",
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" colors=color_mapper.loc[show_techs + [\"Others\"]].tolist(),\n",
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" labels=show_techs+[\"Others\"])\n",
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"\n",
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" axs[1,1].plot(index,\n",
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" energy_inflow.resample(coarse_freq).mean()[show_techs + [\"Others\"]].sum(axis=1).values + \n",
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" energy_outflow.resample(coarse_freq).mean().sum(axis=1).values,\n",
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" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n",
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"\n",
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"\n",
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" axs[1,0].legend()\n",
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" axs[1,1].legend()\n",
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"\n",
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"\n",
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" y_min = pd.concat([\n",
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" energy_outflow.sum(axis=1)]).min()\n",
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" y_max = pd.concat([\n",
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" energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True).max()\n",
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"\n",
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" for ax in axs[:2,:2].flatten():\n",
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" ax.set_ylim(y_min, y_max)\n",
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" ax.set_ylim(y_min, y_max)\n",
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" \n",
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" axs[0,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[0,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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" axs[1,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[1,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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" axs[2,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
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" axs[2,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
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"\n",
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" # -------------------------- electricity prices comparison ----------------------------------\n",
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" prices_col = [col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])]\n",
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" pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n",
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"\n",
|
|
" full_index = pypsa_prices.index\n",
|
|
" \n",
|
|
" coarse_pypsa_prices = pypsa_prices.resample(coarse_freq).mean() \n",
|
|
" pypsa_prices = pypsa_prices.loc[start:end]\n",
|
|
"\n",
|
|
" axs[0,2].plot(pypsa_prices.index, pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n",
|
|
" axs[1,2].plot(coarse_pypsa_prices.index, coarse_pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n",
|
|
"\n",
|
|
" try:\n",
|
|
" entsoe_prices = pd.read_csv(data_path / \"price_data\" / country,\n",
|
|
" index_col=0,\n",
|
|
" parse_dates=True,\n",
|
|
" ).iloc[:-1]\n",
|
|
" def make_tz_time(time):\n",
|
|
" return pd.Timestamp(time).tz_convert(\"utc\")\n",
|
|
"\n",
|
|
" # entsoe_prices.index = pd.Series(entsoe_prices.index).apply(lambda time: make_tz_time(time))\n",
|
|
" entsoe_prices.index = full_index\n",
|
|
" mean_abs_error = mean_absolute_error(entsoe_prices.values,\n",
|
|
" n.buses_t.marginal_price[prices_col].mean(axis=1).values)\n",
|
|
"\n",
|
|
" coarse_prices = entsoe_prices.resample(coarse_freq).mean()\n",
|
|
" entsoe_prices = entsoe_prices.loc[start:end]\n",
|
|
"\n",
|
|
" axs[0,2].plot(entsoe_prices.index, entsoe_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n",
|
|
" axs[1,2].plot(coarse_prices.index, coarse_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n",
|
|
"\n",
|
|
" except FileNotFoundError:\n",
|
|
" mean_abs_error = None\n",
|
|
" pass\n",
|
|
"\n",
|
|
" upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max() \n",
|
|
" for ax in axs[:2,2]:\n",
|
|
" ax.set_ylim(0, upper_lim)\n",
|
|
" ax.set_ylabel(\"Electricty Prices [Euro/MWh]\")\n",
|
|
" ax.legend()\n",
|
|
" \n",
|
|
" if not mean_abs_error is None:\n",
|
|
" axs[1,-1].set_title(f\"Mean Abs Error: {np.around(mean_abs_error, decimals=2)}\") \n",
|
|
" \n",
|
|
" # remaining_cols = energy_inflow.drop(column s=show_techs+[\"Others\"]).columns.tolist()\n",
|
|
" # axs[1,0].set_title(f\"Others: {remaining_cols}\")\n",
|
|
"\n",
|
|
" # ------------------------------- duration curves ------------------------------\n",
|
|
"\n",
|
|
" entsoe_ddf = entsoe_df[show_techs + [\"Others\"]].reset_index(drop=True)\n",
|
|
"\n",
|
|
" entsoe_ddf = pd.concat([\n",
|
|
" entsoe_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in entsoe_ddf.columns\n",
|
|
" ], axis=1)\n",
|
|
"\n",
|
|
" axs[2,0].stackplot(range(len(entsoe_ddf)), *[entsoe_ddf[col].values for col in entsoe_ddf.columns],\n",
|
|
" colors=color_mapper.loc[entsoe_ddf.columns].tolist(),\n",
|
|
" labels=entsoe_ddf.columns)\n",
|
|
"\n",
|
|
" pypsa_ddf = energy_inflow[show_techs + [\"Others\"]].reset_index(drop=True)\n",
|
|
" pypsa_ddf = pd.concat([\n",
|
|
" pypsa_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in pypsa_ddf.columns\n",
|
|
" ], axis=1)\n",
|
|
"\n",
|
|
" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
|
|
" colors=color_mapper.loc[pypsa_ddf.columns].tolist(),\n",
|
|
" labels=pypsa_ddf.columns)\n",
|
|
"\n",
|
|
" ylim_max = max([pypsa_ddf.max(axis=0).sum(), entsoe_ddf.max(axis=0).sum()])\n",
|
|
"\n",
|
|
" pypsa_ddf = energy_outflow.reset_index(drop=True)\n",
|
|
" pypsa_ddf = pd.concat([\n",
|
|
" pypsa_ddf[col].sort_values(ascending=True).reset_index(drop=True) for col in pypsa_ddf.columns\n",
|
|
" ], axis=1)\n",
|
|
"\n",
|
|
" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
|
|
" colors=color_mapper.loc[pypsa_ddf.columns].tolist(),\n",
|
|
" labels=energy_outflow.columns)\n",
|
|
"\n",
|
|
" ylim_min = energy_outflow.min(axis=0).sum()\n",
|
|
"\n",
|
|
" for ax in axs[2,:2]:\n",
|
|
" ax.legend()\n",
|
|
" ax.set_ylim(ylim_min, ylim_max)\n",
|
|
"\n",
|
|
" pypsa_totals = pd.concat([energy_inflow[show_techs + [\"Others\"]], energy_outflow], axis=1).sum() \n",
|
|
"\n",
|
|
" entsoe_totals = entsoe_df.sum()\n",
|
|
" totals = pd.DataFrame(index=pypsa_totals.index) \n",
|
|
" \n",
|
|
" for tech in pypsa_totals.index:\n",
|
|
" if tech not in entsoe_totals.index:\n",
|
|
" entsoe_totals.loc[tech] = 0.\n",
|
|
"\n",
|
|
" totals[\"Pypsa\"] = pypsa_totals\n",
|
|
" totals[\"Entsoe\"] = entsoe_totals\n",
|
|
" totals[\"Technology\"] = totals.index\n",
|
|
"\n",
|
|
" totals = pd.concat([\n",
|
|
" pd.DataFrame({\"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
|
|
" \"Technology\": pypsa_totals.index,\n",
|
|
" \"Total Generation\": pypsa_totals.values,\n",
|
|
" }),\n",
|
|
" pd.DataFrame({\"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
|
|
" \"Technology\": entsoe_totals.index,\n",
|
|
" \"Total Generation\": entsoe_totals.values,\n",
|
|
" }),], axis=0\n",
|
|
" )\n",
|
|
"\n",
|
|
" sns.barplot(data=totals, x=\"Technology\", y=\"Total Generation\", hue=\"Source\", ax=axs[2,2],\n",
|
|
" palette=\"dark\", alpha=.6, edgecolor=\"k\")\n",
|
|
" \n",
|
|
" axs[2,0].set_xlabel(\"Hours\")\n",
|
|
" axs[2,1].set_xlabel(\"Hours\")\n",
|
|
" axs[2,2].set_ylabel(\"Total Generation [GWh]\")\n",
|
|
" axs[2,2].set_xticks(axs[2,2].get_xticks(), axs[2,2].get_xticklabels(), rotation=45, ha='right')\n",
|
|
"\n",
|
|
" corrs = (\n",
|
|
" energy_inflow\n",
|
|
" .corrwith(entsoe_df)\n",
|
|
" .drop(index=\"Others\")\n",
|
|
" .dropna()\n",
|
|
" .sort_values(ascending=False)\n",
|
|
" )\n",
|
|
"\n",
|
|
" for col, ax in zip(corrs.index[:2].tolist() + [corrs.index[-1]], axs[3]):\n",
|
|
" ax.scatter(entsoe_df[col].values,\n",
|
|
" energy_inflow[col].values,\n",
|
|
" color=\"darkred\",\n",
|
|
" alpha=0.5,\n",
|
|
" s=20,\n",
|
|
" edgecolor=\"k\" \n",
|
|
" )\n",
|
|
" ax.set_title(f\"{col}; Pearson Corr {np.around(corrs.loc[col], decimals=4)}\")\n",
|
|
" ax.set_xlabel(\"ENTSO-E Generation [GW]\")\n",
|
|
" ax.set_ylabel(\"PyPSA-Eur Generation [GW]\")\n",
|
|
"\n",
|
|
" for ax in axs[:2].flatten():\n",
|
|
" ax.set_xlabel(\"Datetime\")\n",
|
|
"\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.savefig(plot_path / (cc+\".pdf\")) \n",
|
|
" \n",
|
|
" plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# pypsa_total_inflow.to_csv(\"total_inflow_pypsa.csv\")\n",
|
|
"# pypsa_total_outflow.to_csv(\"total_outflow_pypsa.csv\")\n",
|
|
"# entsoe_total_inflow.to_csv(\"total_inflow_entsoe.csv\")\n",
|
|
"# total_load.to_csv(\"total_load.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.metrics import mean_absolute_error\n",
|
|
"from tqdm import tqdm\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"plt.style.use(\"ggplot\")\n",
|
|
"import seaborn as sns\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"entsoe_total_inflow = pd.read_csv(\"total_inflow_entsoe.csv\", index_col=0, parse_dates=True)\n",
|
|
"pypsa_total_inflow = pd.read_csv(\"total_inflow_pypsa.csv\", index_col=0, parse_dates=True)\n",
|
|
"pypsa_total_outflow = pd.read_csv(\"total_outflow_pypsa.csv\", index_col=0, parse_dates=True)\n",
|
|
"total_load = pd.read_csv(\"total_load.csv\", index_col=0, parse_dates=True)\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
|
|
"\n",
|
|
"# pypsa_totals = pd.concat([pypsa_total_inflow, pypsa_total_outflow], axis=1).sum() * 1e-3 \n",
|
|
"pypsa_totals = pypsa_total_inflow.drop(columns=[\"Inflow Lines\", \"Inflow Links\"]).sum() * 1e-3\n",
|
|
"\n",
|
|
"entsoe_totals = entsoe_total_inflow.drop(columns=[\"Inflow Lines\", \"Inflow Links\"]).sum() * 1e-3\n",
|
|
"totals = pd.DataFrame(index=pypsa_totals.index) \n",
|
|
"\n",
|
|
"for tech in pypsa_totals.index:\n",
|
|
" if tech not in entsoe_totals.index:\n",
|
|
" entsoe_totals.loc[tech] = 0.\n",
|
|
"\n",
|
|
"totals[\"Pypsa\"] = pypsa_totals\n",
|
|
"totals[\"Entsoe\"] = entsoe_totals\n",
|
|
"totals[\"Technology\"] = totals.index\n",
|
|
"\n",
|
|
"totals = pd.concat([\n",
|
|
" pd.DataFrame({\"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
|
|
" \"Technology\": pypsa_totals.index,\n",
|
|
" \"Total Generation\": pypsa_totals.values,\n",
|
|
" }),\n",
|
|
" pd.DataFrame({\"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
|
|
" \"Technology\": entsoe_totals.index,\n",
|
|
" \"Total Generation\": entsoe_totals.values,\n",
|
|
" }),], axis=0\n",
|
|
")\n",
|
|
"\n",
|
|
"\n",
|
|
"sns.barplot(data=totals, x=\"Technology\", y=\"Total Generation\", hue=\"Source\", ax=ax,\n",
|
|
" palette=\"dark\", alpha=.6, edgecolor=\"k\")\n",
|
|
"ax.set_ylabel(\"Total Generation [TWh]\")\n",
|
|
"ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')\n",
|
|
"plt.savefig(plot_path / \"EuropeTotalGeneration.pdf\")\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"pypsa_total_inflow = pypsa_total_inflow.drop(columns=[\"Inflow Links\", \"Inflow Lines\"])\n",
|
|
"pypsa_total_outflow = pypsa_total_outflow.drop(columns=[\"Outflow Links\", \"Outflow Lines\"])\n",
|
|
"entsoe_total_inflow = entsoe_total_inflow.drop(columns=[\"Inflow Links\", \"Inflow Lines\"])\n",
|
|
"\n",
|
|
"start = pd.Timestamp(\"2019-01-01\") # for small time frame\n",
|
|
"end = pd.Timestamp(\"2019-01-14\")\n",
|
|
"coarse_freq = \"3d\"\n",
|
|
"\n",
|
|
"index = load.loc[start:end].index\n",
|
|
"cols = pypsa_total_inflow.std(axis=0).sort_values(ascending=True).index\n",
|
|
"cols_out = pypsa_total_outflow.std(axis=0).sort_values(ascending=False).index\n",
|
|
"\n",
|
|
"fig, axs = plt.subplots(4, 2, figsize=(20, 30))\n",
|
|
"\n",
|
|
"axs[0,0].stackplot(index, *[entsoe_total_inflow[col].loc[start:end].values for col in cols],\n",
|
|
" colors=color_mapper.loc[cols].tolist(), \n",
|
|
" )\n",
|
|
"\n",
|
|
"axs[0,1].stackplot(index, *[pypsa_total_inflow[col].loc[start:end].values for col in cols],\n",
|
|
" colors=color_mapper.loc[cols].tolist(), \n",
|
|
" )\n",
|
|
"axs[0,1].stackplot(index, *[pypsa_total_outflow[col].loc[start:end].values for col in cols_out],\n",
|
|
" colors=color_mapper.loc[cols_out].tolist(), \n",
|
|
" )\n",
|
|
"\n",
|
|
"entsoe_total_inflow = entsoe_total_inflow.resample(coarse_freq).mean()\n",
|
|
"pypsa_total_inflow = pypsa_total_inflow.resample(coarse_freq).mean()\n",
|
|
"pypsa_total_outflow = pypsa_total_outflow.resample(coarse_freq).mean()\n",
|
|
"\n",
|
|
"index = pypsa_total_inflow.index\n",
|
|
"\n",
|
|
"axs[1,0].stackplot(index, *[entsoe_total_inflow[col].values for col in cols],\n",
|
|
" colors=color_mapper.loc[cols].tolist(), \n",
|
|
" )\n",
|
|
"\n",
|
|
"axs[1,1].stackplot(index, *[pypsa_total_inflow[col].values for col in cols],\n",
|
|
" colors=color_mapper.loc[cols].tolist(), \n",
|
|
" )\n",
|
|
"axs[1,1].stackplot(index, *[pypsa_total_outflow[col].values for col in cols_out],\n",
|
|
" colors=color_mapper.loc[cols_out].tolist(), \n",
|
|
" )\n",
|
|
"\n",
|
|
"for ax in axs[:3].flatten():\n",
|
|
" ax.set_ylim(-100, 400)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"total_entsoe_ddf = pd.concat([\n",
|
|
" entsoe_total_inflow[col]\n",
|
|
" .sort_values(ascending=False)\n",
|
|
" .reset_index(drop=True) for col in entsoe_total_inflow.columns\n",
|
|
"], axis=1)\n",
|
|
"axs[2,0].stackplot(range(len(total_entsoe_ddf)), *[total_entsoe_ddf[col].values for col in total_entsoe_ddf.columns],\n",
|
|
" colors=color_mapper.loc[total_entsoe_ddf.columns].tolist(),\n",
|
|
" labels=total_entsoe_ddf.columns)\n",
|
|
"\n",
|
|
"total_pypsa_ddf = pd.concat([\n",
|
|
" pypsa_total_inflow[col]\n",
|
|
" .sort_values(ascending=False)\n",
|
|
" .reset_index(drop=True) for col in pypsa_total_inflow.columns\n",
|
|
"], axis=1)\n",
|
|
"axs[2,1].stackplot(range(len(total_pypsa_ddf)), *[total_pypsa_ddf[col].values for col in total_pypsa_ddf.columns],\n",
|
|
" colors=color_mapper.loc[total_pypsa_ddf.columns].tolist(),\n",
|
|
" labels=total_pypsa_ddf.columns)\n",
|
|
"\n",
|
|
"total_pypsa_ddf = pd.concat([\n",
|
|
" pypsa_total_outflow[col]\n",
|
|
" .sort_values(ascending=False)\n",
|
|
" .reset_index(drop=True) for col in pypsa_total_outflow.columns\n",
|
|
"], axis=1)\n",
|
|
"axs[2,1].stackplot(range(len(total_pypsa_ddf)), *[total_pypsa_ddf[col].values for col in total_pypsa_ddf.columns],\n",
|
|
" colors=color_mapper.loc[total_pypsa_ddf.columns].tolist(),\n",
|
|
" labels=total_pypsa_ddf.columns)\n",
|
|
"axs[2,0].legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n",
|
|
" fancybox=True, shadow=True, ncol=5)\n",
|
|
"\n",
|
|
"total_prices = (\n",
|
|
" n.buses_t.marginal_price\n",
|
|
" .multiply(n.loads_t.p_set)\n",
|
|
" .sum(axis=1)\n",
|
|
" .divide(n.loads_t.p_set.sum(axis=1))\n",
|
|
")\n",
|
|
"\n",
|
|
"total_entsoe_prices = None\n",
|
|
"\n",
|
|
"for num, country in tqdm(enumerate(os.listdir(data_path / \"pypsa_data\"))):\n",
|
|
"\n",
|
|
" cc = country[:2]\n",
|
|
" country_buses = np.unique(n.generators.loc[n.generators.bus.str.contains(cc)].bus.values)\n",
|
|
"\n",
|
|
" if not len(country_buses) == 1:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" bus = country_buses[0]\n",
|
|
"\n",
|
|
" try:\n",
|
|
" entsoe_prices = pd.read_csv(data_path / \"price_data\" / country,\n",
|
|
" index_col=0,\n",
|
|
" parse_dates=True,\n",
|
|
" ).iloc[:-1]\n",
|
|
" entsoe_prices.index = n.loads_t.p_set.index\n",
|
|
" def make_tz_time(time):\n",
|
|
" return pd.Timestamp(time).tz_convert(\"utc\")\n",
|
|
"\n",
|
|
" except FileNotFoundError: \n",
|
|
" continue\n",
|
|
"\n",
|
|
" if total_entsoe_prices is None:\n",
|
|
" total_entsoe_prices = pd.Series(np.zeros(len(entsoe_prices)), index=entsoe_prices.index)\n",
|
|
"\n",
|
|
" total_entsoe_prices += entsoe_prices.iloc[:,0] * n.loads_t.p_set[bus]\n",
|
|
"\n",
|
|
"total_entsoe_prices /= n.loads_t.p_set.sum(axis=1)\n",
|
|
"\n",
|
|
"error = np.around(mean_absolute_error(total_entsoe_prices.values, total_prices.values),\n",
|
|
" decimals=2)\n",
|
|
"\n",
|
|
"axs[3,0].plot(total_prices.loc[start:end].index, total_prices.loc[start:end].values,\n",
|
|
" label=\"PyPSA Marginal Price\")\n",
|
|
"axs[3,0].plot(total_prices.loc[start:end].index, total_entsoe_prices.loc[start:end].values, \n",
|
|
" label=\"ENTSO-E\")\n",
|
|
"axs[3,1].set_title(f\"Mean Abs Error {error} [Euro/MWh]\")\n",
|
|
"axs[3,0].legend()\n",
|
|
"\n",
|
|
"total_prices = total_prices.resample(coarse_freq).mean()\n",
|
|
"total_entsoe_prices = total_entsoe_prices.resample(coarse_freq).mean()\n",
|
|
"\n",
|
|
"axs[3,1].plot(total_prices.index, total_prices.values, label=f\"PyPSA Marginal Price\")\n",
|
|
"axs[3,1].plot(total_prices.index, total_entsoe_prices.values, label=\"ENTSO-E Price\")\n",
|
|
"\n",
|
|
"axs[3,1].legend()\n",
|
|
"\n",
|
|
"for ax in axs[:3,0]:\n",
|
|
" ax.set_ylabel(\"ENTSO-E Generation [GWh]\")\n",
|
|
"for ax in axs[:3,1]:\n",
|
|
" ax.set_ylabel(\"PyPSA Generation [GWh]\")\n",
|
|
"for ax in axs[:2].flatten():\n",
|
|
" ax.set_xlabel(\"Datetime\")\n",
|
|
"for ax in axs[3]:\n",
|
|
" ax.set_xlabel(\"Datetime\")\n",
|
|
" ax.set_ylabel(\"Cost of Electricity [Euro/MWh]\")\n",
|
|
"for ax in axs[2]:\n",
|
|
" ax.set_xlabel(\"Hour\")\n",
|
|
"\n",
|
|
"plt.savefig(plot_path / \"EuropeDashboard.pdf\")\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "pypsa-eur",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.10"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|